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We have developed GoMiner, a program package that organizes lists of 'interesting' genes (for example, under- and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. GoMiner provides quantitative and statistical output files and two useful visualizations. The first is a tree-like structure(More)
We have designed and developed a Gene Ontology based navigation tool, GoMiner, which organizes lists of interesting genes from a microarray or a protein array experiment for biological interpretation. It provides quantitative and statistical output files and useful visualization (e.g., a tree-like structure) to map the list of genes to its biological(More)
EGOMiner, stands for enhanced gene ontology mining system, is a CORBA based tool developed for genomic and proteomic data analysis. One key function of EGOMiner is to provide biological data interpretation based on gene ontology with quantitative and statistical analysis and visualization by direct acyclic graph. Cross comparison of multiple experimental(More)
EGOMiner, stands for enhanced gene ontology mining system, is a CORBA based tool developed for genomic and proteomic data analysis. One key function of EGOMiner is to provide biological data interpretation based on gene ontology with quantitative and statistical analysis and visualization by direct acyclic graph. Cross comparison of multiple experimental(More)
Comparing genes expressed in normal and diseased states assists the understanding of cancer pathophysiology, detection, prognosis, and therapeutic target study. Many existing expression analysis papers show that microarray data are usually case dependent, have small sample (patients) sizes, and have large gene dimensions. Thus, we have been developing a(More)
A visualization and steering application, GAVis, has been developed to aid in understanding the behavior of and guiding the convergence of genetic algorithms running in parallel over long time periods. When classification techniques such as Support Vector Machines (SVMs) paired with complete leave-one- out validation are used as a fitness function for(More)
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